By binding to specific structures on antigenic proteins, the so-called epitopes, B-cell antibodies can neutralize pathogens. The identification of B-cell epitopes is of great value for the development of specific serodiagnostic assays and the optimization of medical therapy. However, identifying diagnostically or therapeutically relevant epitopes is a challenging task that usually involves extensive laboratory work. In this study, we show that the time, cost and labor-intensive process of epitope detection in the lab can be significantly reduced by using in silico prediction.

中文翻译：

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EpiDope：用于线性B细胞表位预测的深度神经网络。

通过结合抗原蛋白的特定结构，即所谓的表位，B细胞抗体可以中和病原体。B细胞表位的鉴定对于开发特定的血清诊断方法和优化药物治疗具有重要价值。然而，鉴定与诊断或治疗相关的表位是一项艰巨的任务，通常涉及大量的实验室工作。在这项研究中，我们表明使用计算机模拟预测可以显着减少实验室中抗原决定簇检测的时间，成本和劳动密集型过程。